{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import gym\n",
    "import itertools\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "\n",
    "if \"../\" not in sys.path:\n",
    "  sys.path.append(\"../\") \n",
    "\n",
    "from collections import defaultdict\n",
    "from lib.envs.windy_gridworld import WindyGridworldEnv\n",
    "from lib import plotting\n",
    "\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = WindyGridworldEnv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_epsilon_greedy_policy(Q, epsilon, nA):\n",
    "    \"\"\"\n",
    "    Creates an epsilon-greedy policy based on a given Q-function and epsilon.\n",
    "    \n",
    "    Args:\n",
    "        Q: A dictionary that maps from state -> action-values.\n",
    "            Each value is a numpy array of length nA (see below)\n",
    "        epsilon: The probability to select a random action . float between 0 and 1.\n",
    "        nA: Number of actions in the environment.\n",
    "    \n",
    "    Returns:\n",
    "        A function that takes the observation as an argument and returns\n",
    "        the probabilities for each action in the form of a numpy array of length nA.\n",
    "    \n",
    "    \"\"\"\n",
    "    def policy_fn(observation):\n",
    "        A = np.ones(nA, dtype=float) * epsilon / nA\n",
    "        best_action = np.argmax(Q[observation])\n",
    "        A[best_action] += (1.0 - epsilon)\n",
    "        return A\n",
    "    return policy_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sarsa(env, num_episodes, discount_factor=1.0, alpha=0.5, epsilon=0.1):\n",
    "    \"\"\"\n",
    "    SARSA algorithm: On-policy TD control. Finds the optimal epsilon-greedy policy.\n",
    "    \n",
    "    Args:\n",
    "        env: OpenAI environment.\n",
    "        num_episodes: Number of episodes to run for.\n",
    "        discount_factor: Gamma discount factor.\n",
    "        alpha: TD learning rate.\n",
    "        epsilon: Chance the sample a random action. Float betwen 0 and 1.\n",
    "    \n",
    "    Returns:\n",
    "        A tuple (Q, stats).\n",
    "        Q is the optimal action-value function, a dictionary mapping state -> action values.\n",
    "        stats is an EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\n",
    "    \"\"\"\n",
    "    \n",
    "    # The final action-value function.\n",
    "    # A nested dictionary that maps state -> (action -> action-value).\n",
    "    Q = defaultdict(lambda: np.zeros(env.action_space.n))\n",
    "    \n",
    "    # Keeps track of useful statistics\n",
    "    stats = plotting.EpisodeStats(\n",
    "        episode_lengths=np.zeros(num_episodes),\n",
    "        episode_rewards=np.zeros(num_episodes))\n",
    "\n",
    "    # The policy we're following\n",
    "    policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\n",
    "    \n",
    "    for i_episode in range(num_episodes):\n",
    "        # Print out which episode we're on, useful for debugging.\n",
    "        if (i_episode + 1) % 100 == 0:\n",
    "            print(\"\\rEpisode {}/{}.\".format(i_episode + 1, num_episodes), end=\"\")\n",
    "            sys.stdout.flush()\n",
    "        \n",
    "        # Reset the environment and pick the first action\n",
    "        state = env.reset()\n",
    "        action_probs = policy(state)\n",
    "        action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\n",
    "        \n",
    "        # One step in the environment\n",
    "        for t in itertools.count():\n",
    "            # Take a step\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            \n",
    "            # Pick the next action\n",
    "            next_action_probs = policy(next_state)\n",
    "            next_action = np.random.choice(np.arange(len(next_action_probs)), p=next_action_probs)\n",
    "            \n",
    "            # Update statistics\n",
    "            stats.episode_rewards[i_episode] += reward\n",
    "            stats.episode_lengths[i_episode] = t\n",
    "            \n",
    "            # TD Update\n",
    "            td_target = reward + discount_factor * Q[next_state][next_action]\n",
    "            td_delta = td_target - Q[state][action]\n",
    "            Q[state][action] += alpha * td_delta\n",
    "    \n",
    "            if done:\n",
    "                break\n",
    "                \n",
    "            action = next_action\n",
    "            state = next_state        \n",
    "    \n",
    "    return Q, stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 200/200."
     ]
    }
   ],
   "source": [
    "Q, stats = sarsa(env, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pF4LrtkzK4sWLcfjwYeTk5CAwMBDjx49Hv379sHDhQqSlpSEkJARz5syxT8RYuXIlDhw4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\nioro0KEDc+bM4f3332fFihX07duXO+64g8jIyHrbFxUVER4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1061fa358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GrXGt5cUpbxGTlZVlNerP39+/wn2kqObK7lNFREREdZ9T1gRWhrVYRERERJc4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105b8e470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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99tnKzEdERGRT5PQJy0ERnbpD9XiQL7mSzShzqQsJCcGkSZOQkZGBc+fOISAg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\nMCZyULHb/RYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105fd02e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting.plot_episode_stats(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
